With the rise of data driven application scenarios, such as machine learning, there is an opportunity to enable sharing of master data (e.g., as training data). While access to data in general has improved, data sharing is still not a seamless activity and is, for example, a major barrier for value capturing in areas such as the U.S. healthcare market. Moreover, there are risks when sharing data because data may reveal insights into a company's business model or sensitive information about individuals. Furthermore, there are ethical considerations in regards to the decisions of machine learning approaches like neural networks (e.g., what influence the data of an individual may have in the training data). Research has shown that these risks also hold when data is anonymized with improper suppression methods by re-identification attacks.